1. Field
The following description relates to an apparatus and a method for effective graph clustering of probabilistic graphs.
2. Description of the Related Art
The development of computer technologies have accelerated the generation, use and analysis of various types of data, such as text, image, audio, video, sequence, and high-dimensional data. A graph is an abstract data type that may be used to represent such data, and may represent a data element as a node and a relationship between data elements as an edge. In various fields, such as social networking and genetic analysis, data representation and analysis technologies based on such a graph have been utilized.
Clustering is a method of analyzing graph data, and may be classified into node clustering and graph clustering. Node clustering is a clustering method of partitioning a single large graph into a plurality of dense sub-graphs, and graph clustering is a clustering method of gathering small graphs including similar shapes.
Research on node clustering has been relatively actively conducted, but research on graph clustering has been hardly conducted. In addition, since a graph cannot reflect uncertainty of the real world, a real-world phenomenon may be restrictively modeled, and therefore, accuracy of data may be deteriorated resulting in deterioration in accuracy of clustering.